Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit

Yao, Dechen; Li, Boyang; Liu, Hengchang; Yang, Jianwei; Jia, Limin

doi:10.1016/j.measurement.2021.109166

Cited by 97 publications

(32 citation statements)

References 35 publications

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“…In this section, three other state-of-the-art point estimation prognostics approaches are firstly implemented to predict RUL for bearings for comparison, which are named as N1, N2 and N3, respectively. Among them, N1 [28] first converts the time-domain signal into the frequency-domain, then inputs it into one-dimensional CNN and simple recurrent unit network to realize RUL prediction. N2 [29] utilizes LSTM and attention mechanism to learn degradation representations from original monitoring data and predict RUL.…”

Section: Comparison With the State-of-the-art Prognostics Methodsmentioning

confidence: 99%

Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

Wang

Lei

Yan

et al. 2021

JDMD

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Recently, deep learning is widely used in the field of remaining useful life (RUL) prediction. Among various deep learning technologies, recurrent neural network (RNN) and its variant, e.g., long short-term memory (LSTM) network, are gaining more attention because of their capability of capturing temporal dependence. Although the existing RNN-based approaches have demonstrated their RUL prediction effectiveness, they still suffer from the following two limitations: 1) it is difficult for RNN to extract directly degradation features from original monitoring data, and 2) most of the RNN-based prognostics methods are unable to quantify the uncertainty of prediction results. To address the above limitations, this paper proposes a new method named Residual convolution LSTM (RC-LSTM) network. In RC-LSTM, a new ResNet-based convolution LSTM (Res-ConvLSTM) layer is stacked with convolution LSTM (ConvLSTM) layer to extract degradation representations from monitoring data. Then, predicated on the RUL following a normal distribution, an appropriate output layer is constructed to quantify the uncertainty of the forecast result. Finally, the effectiveness and superiority of RC-LSTM is verified using monitoring data from accelerated degradation tests of rolling element bearings.

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Section: Comparison With the State-of-the-art Prognostics Methodsmentioning

confidence: 99%

Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

Wang

Lei

Yan

et al. 2021

JDMD

View full text Add to dashboard Cite

show abstract

“…It takes full advantage of the characteristics of the CNN network integrating automatic signal feature extraction, signal dimensionality reduction, feature selection and pattern classification to achieve "end-to-end" prediction. Prior studies have adopted the above scheme and verified the algorithm's validity [24,25].…”

Section: Implementation Scheme Of Cnn For F107 Predictionmentioning

confidence: 99%

Forecasting the 10.7-cm Solar Radio Flux Using Deep CNN-LSTM Neural Networks

et al. 2022

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Predicting the time series of 10.7-cm solar radio flux is a challenging task because of its daily variability. This paper proposed a non-linear method, a convolutional and recurrent neural network combined model to achieve end-to-end F10.7 forecasts. The network consists of a one-dimensional convolutional neural network and a long short-term memory network. The CNN network extracted features from F10.7 original data, then trained the feature signals in the long short-term memory network, and outputted the predicted values. The F10.7 daily data during 2003–2014 are used for the testing set. The mean absolute percentage error values of approximately 2.04%, 2.78%, and 4.66% for 1-day, 3-day, and 7-day forecasts, respectively. The statistical results of evaluating the root mean square error, spearman correlation coefficient shows a superior effect as a whole for the 1–27 days forecast, compared with the ordinary single neural network and combination models.

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“…In the problem of predicting the remaining life of mechanical equipment based on a supervised learning model, adding labels to the data is equivalent to modeling the degradation state of the equipment. At present, there are two main ways to add labels [38][39][40][41][42]. One is to use a linear function, as shown in Figure 12a.…”

Section: Prediction Of the Rul Based On 1d-cnnmentioning

confidence: 99%

“…In order to verify the advantages of this method, two common ways of adding labels selected for comparison. In [41], the method of adding labels in Figure 12a was selected, named Method2. The method of adding labels in [39] is shown in Figure 12b, named Method3.…”

Section: Prediction Of the Rul Based On 1d-cnnmentioning

confidence: 99%

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

et al. 2021

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Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method’s effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend.

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Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit

Cited by 97 publications

References 35 publications

Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

Forecasting the 10.7-cm Solar Radio Flux Using Deep CNN-LSTM Neural Networks

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

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